The analysis of visual images by computational methods is of obvious commercial value. Visual images can be represented in the form of an array of numerical values, which values represent the luminosity of points in space. If the observed region contains physical objects, information about the position arrangement of physical objects in the observed region is encoded as numerical changes in luminosity, distributed over space and time. The individual numerical picture elements are called “pixels.”
The mathematical concept of a gradient is of manifest value when applied to numerical image data. A gradient is a property of an ordered collection of numerical quantities. The ordering principle (or independent variable) can be space, or time, or some combination of these or some other organizing dimension of a conceptual framework appropriate to the phenomenon being described.
A gradient can be said to exist when numerical data undergoes some sort of change; that is to say, when the numerical value representing the data changes over time, or space, or some other independent variable. As it relates to video image data, the concept of a spatial gradient is of seminal importance. A spatial gradient of luminosity often exists along the boundaries of distinct separate physical objects that are contained within the observed region, or along the boundaries of various component parts of which these objects are composed. Such spatial gradients can be used to discover physical objects within the region observed by a video camera or other imaging instrument. The spatial extent of these objects can be discerned by resolving the set of pixels that comprise the image into a plurality of subsets wherein all the pixels within each subset correspond to, and are associated with, an identifiable physical object, or an identifiable component part of a physical object.
While it is known that spatial gradients may be used to identify the shape and position of an object in an image, optical detectors are often not configured in a manner that maximizes the capture of relevant optical information associated with the object. For example, consider an optical image of a face of building that contains a window. Spatial gradients may be used to identify an object, i.e., the window, in the image. However, if the portion of the image containing the window was shadowed by another building, the window may appear as completely dark in the image, in essence, containing no optical information. Although there may be a person at the window, the optical detector is unable to sense an image of the person because of limitations in the dynamic range of the detector elements that correspond to the window. The present invention addresses this shortcoming, by providing functionality that facilitates detection of optical information that might otherwise be outside of the dynamic range of the relevant detector elements.